Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges wi...
详细信息
ISBN:
(纸本)9798350377859;9798350377842
Layout optimization (LO) is gaining increasing attention as competition among enterprises intensifies and the demand for cost reduction and efficiency improvement continues to grow. LO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various LO applications, they focus only on the diversity and convergence of the objective space, neglecting the diversity of solutions, which is detrimental to solving potential issues. Therefore, this paper proposes a constrained multimodal multiobjective optimization (CMMO) framework based on dual-population multi-task collaborative evolution. This framework assists in solving the workshop layout problem by creating an auxiliary task population and transferring knowledge to support the optimization process. Additionally, the dynamic narrowing of constraint boundaries and gradual expansion of the contrast neighborhood for the auxiliary tasks ensure a high degree of correlation with the main task, continuously providing supplementary evolutionary directions. Ultimately, compared to other constrained multimodal multiobjective algorithms and two classic genetic algorithms, the proposed algorithm proves to be more competitive in practical layout optimization problems.
Convenience stores, as a rapidly developing new retail format, have a significant impact on both consumer convenience and the brand's commercial profits and logistics costs. With the rise of online food delivery a...
详细信息
ISBN:
(纸本)9798400711459
Convenience stores, as a rapidly developing new retail format, have a significant impact on both consumer convenience and the brand's commercial profits and logistics costs. With the rise of online food delivery and other services, convenience stores need to optimize their location selection and spatial layout to meet modern consumers' demands and enhance market competitiveness. This paper takes the Everyday Chain as the research subject, analyzing its spatial distribution and influencing factors in the main urban area of Xi'an. A Maximum Coverage Location Model is adopted, combined with deep reinforcement learning and genetic algorithms for optimization. The study results indicate that deep reinforcement learning outperforms genetic algorithms regarding solution efficiency and coverage performance, offering a new approach and reference for convenience store location optimization. This can better enhance the rationality of service layout and the market competitiveness of convenience stores.
The article considers a comprehensive comparison of optimizationalgorithms for solving the problem of placing vector graphic objects on a plane. algorithms are described in the form of block diagrams, which allows an...
详细信息
The workshop on Quantum in Consumer Technology at IEEE Quantum Week 2024 was an inspiring event that united experts from diverse fields to explore the present advancements and future possibilities of quantum technolog...
详细信息
The workshop on Quantum in Consumer Technology at IEEE Quantum Week 2024 was an inspiring event that united experts from diverse fields to explore the present advancements and future possibilities of quantum technology in consumer applications. Organized by the Quantum in Consumer Technology Technical Committee of the IEEE Consumer Technology Society (CTSoc), this workshop focused on the integration and application of quantum technologies in consumer electronics, exploring current innovations and future directions;see CTSoc representatives in Figure 1(a). This workshop was part of the IEEE Quantum Week 2024, officially known as the IEEE international Conference on Quantum Computing and Engineering (QCE24), which was held in the Palais des Congr & egrave;s in Montr & eacute;al, Qu & eacute;bec, Canada, from 15 to 20 September, 2024.
Due to the incorrect proportions between the exploitation and exploration phases, the whale optimization algorithm (WOA) gets stuck into the local optima, which causes premature convergence. To address this issue, qua...
详细信息
Intelligent optimization is a kind of global optimizationalgorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorith...
详细信息
ISBN:
(纸本)9783031096778;9783031096761
Intelligent optimization is a kind of global optimizationalgorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimizationalgorithms have been proposed based on a large mount of animals' or plants' behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms' analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimizationalgorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimizationalgorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.
Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given...
详细信息
ISBN:
(纸本)9798400704901
Nowadays, while most machine learning research on customer journey optimization has focused on short-term success metrics such as click-through rates and optimal ad placement, there has been little consideration given to developing a coherent system for end-to-end customer journey optimization. Such a system would encompass all aspects of the customer experience, from presenting the right product value to the right users, to understanding a user's likelihood of conversion and long-term value to the platform, as well as their propensity for cross-selling and risk of churning. Currently, models and algorithms for customer journey optimization are often developed in isolation, leading to inefficiencies in modeling and data pipelines. Furthermore, the customer is often viewed as a collection of different entities by different organizational departments (such as marketing, sales, and finance), which can lead to additional friction in the customer experience. This workshop seeks to bridge the gap between academic researchers and industrial practitioners who are interested in building holistic solutions for end-to-end customer journey optimization. In addition, with the rising popularity of generative AI and LLM, we want to use this venue to exchange ideas regarding their applications in different stages of customer journey, and how the new technologies could help businesses achieve their KPIs.
Multi-objective evolutionary algorithms have been shown to solve multi-objective optimization problems well and have been very widely used, but there are still drawbacks such as failure to develop sufficient environme...
详细信息
Flexible job shop scheduling problem is a NP-hard combinatorial optimization problem, which has significant applications in the field of workshop scheduling and intelligent manufacturing. Due to its complexity and sig...
详细信息
Flexible job shop scheduling problem is a NP-hard combinatorial optimization problem, which has significant applications in the field of workshop scheduling and intelligent manufacturing. Due to its complexity and significance, lots of attention have been paid to tackle this problem. This paper reviews some of the researches on this problem, by presenting and classifying the different criteria, constraints, and solution approaches. The existing solution methods for the flexible job shop scheduling problem in this literature are classified into exact algorithms, heuristics, and meta-heuristics, which are thoroughly reviewed. Particularly, the paper highlights the flexible job shop scheduling problem in the context of dynamic events and preventive maintenance. These dynamic events, such as machine breakdowns and unexpected changes in job requirements, present additional challenges to the scheduling problem. Furthermore, this paper analyzes the development trends in the manufacturing industry and summarizes detailed future research opportunities for the flexible job shop scheduling problem.
Automatic guide car is one of the key equipment to realize automatic production in intelligent manufacturing workshop, and it is an important logistics carrier. In order to improve the automatic production efficiency ...
详细信息
暂无评论